class_docstr: EarlyStoppingHook will stop training when a given metric has plateaued.
code: def _process_metrics(self, global_step, metrics):
      if not metrics:
          return None
  
      if not list(metrics.values())[0]:
          return None
  
      # Metrics should have just a single subdir and a single tag
      steps, vals = list(metrics.values())[0][self._tags[0]]
      return has_metric_plateaued(
          steps,
          vals,
          num_steps=self._num_plateau_steps,
          delta=self._plateau_delta,
          decrease=self._plateau_decrease,
      )
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import os
  import tensorflow.compat.v1
  from tensorboard.backend.event_processing import event_accumulator
  from tensorboard.backend.event_processing import event_multiplexer
label_desc: Use image node_id 2 for calling the EarlyStoppingHook obj's underlying member method code with example usage: obj._process_metrics(global_step, metrics) and returns: has_metric_plateaued, None, None